Multi-scale modelling of granular avalanches

Avalanches, debris flows, and landslides are geophysical hazards, which involve rapid mass movement of granular solids, water and air as a single-phase system. The dynamics of a granular flow involve at least three distinct scales: the micro-scale, meso-scale, and the macro-scale. This study aims to understand the ability of continuum models to capture the micro-mechanics of dry granular collapse. Material Point Method (MPM), a hybrid Lagrangian and Eulerian approach, with Mohr-Coulomb failure criterion is used to describe the continuum behaviour of granular column collapse, while the micromechanics is captured using Discrete Element Method (DEM) with tangential contact force model. The run-out profile predicted by the continuum simulations matches with DEM simulations for columns with small aspect ratios ('h/r' < 2), however MPM predicts larger run-out distances for columns with higher aspect ratios ('h/r' > 2). Energy evolution studies in DEM simulations reveal higher collisional dissipation in the initial free-fall regime for tall columns. The lack of a collisional energy dissipation mechanism in MPM simulations results in larger run-out distances. Micro-structural effects, such as shear band formations, were observed both in DEM and MPM simulations. A sliding flow regime is observed above the distinct passive zone at the core of the column. Velocity profiles obtained from both the scales are compared to understand the reason for a slow flow run-out mobilization in MPM simulations. © 2013 AIP Publishing LLC.

Multi scale shape index for 3D object recognition

We present Multi Scale Shape Index (MSSI), a novel feature for 3D object recognition. Inspired by the scale space filtering theory and Shape Index measure proposed by Koenderink & Van Doorn [6], this feature associates different forms of shape, such as umbilics, saddle regions, parabolic regions to a real valued index. This association is useful for representing an object based on its constituent shape forms. We derive closed form scale space equations which computes a characteristic scale at each 3D point in a point cloud without an explicit mesh structure. This characteristic scale is then used to estimate the Shape Index. We quantitatively evaluate the robustness and repeatability of the MSSI feature for varying object scales and changing point cloud density. We also quantify the performance of MSSI for object category recognition on a publicly available dataset. © 2013 Springer-Verlag.

Multi-scale permeability of murine bone measured by nanoindentation

The determination of lacunar-canalicular permeability is essential to understand the mechano-transduction mechanism of bone. Murine models are widely used to investigate skeletal growth and regulation, but the value of lacunar-canalicular permeability is still unclear. To address this question, a poroelastic analysis based on nanoindentation data was used to calculate the lacunar-canalicular permeability of wild type C57BL/6 mice of 12 months. Cross-sections of three tibiae were indented using spherical fluid cell indenter tips of two sizes. Results suggest that the value of lacunar-canalicular intrinsic permeability of B6 female murine tibia is in the order of 10 -24 m2. The distribution of the values of intrinsic permeability suggests that with larger contact sizes, nanoindentation alone is capable of capturing the multi-scale permeability of bone. Multi-scale permeability of bone measured by nanoindentation will lead to a better understanding of the role of fluid flow in mechano-transduction. © 2013 American Society of Civil Engineers.

Multi-Scale Vector-Ridge-Detection for Perceptual Organization Without Edges

We present a novel ridge detector that finds ridges on vector fields. It is designed to automatically find the right scale of a ridge even in the presence of noise, multiple steps and narrow valleys. One of the key features of such ridge detector is that it has a zero response at discontinuities. The ridge detector can be applied to scalar and vector quantities such as color. We also present a parallel perceptual organization scheme based on such ridge detector that works without edges; in addition to perceptual groups, the scheme computes potential focus of attention points at which to direct future processing. The relation to human perception and several theoretical findings supporting the scheme are presented. We also show results of a Connection Machine implementation of the scheme for perceptual organization (without edges) using color.

Multi-scale 3D Scene Flow from Binocular Stereo Sequences

Scene flow methods estimate the three-dimensional motion field for points in the world, using multi-camera video data. Such methods combine multi-view reconstruction with motion estimation approaches. This paper describes an alternative formulation for dense scene flow estimation that provides convincing results using only two cameras by fusing stereo and optical flow estimation into a single coherent framework. To handle the aperture problems inherent in the estimation task, a multi-scale method along with a novel adaptive smoothing technique is used to gain a regularized solution. This combined approach both preserves discontinuities and prevents over-regularization-two problems commonly associated with basic multi-scale approaches. Internally, the framework generates probability distributions for optical flow and disparity. Taking into account the uncertainty in the intermediate stages allows for more reliable estimation of the 3D scene flow than standard stereo and optical flow methods allow. Experiments with synthetic and real test data demonstrate the effectiveness of the approach.

Multi-Scale 3D Scene Flow from Binocular Stereo Sequences

Scene flow methods estimate the three-dimensional motion field for points in the world, using multi-camera video data. Such methods combine multi-view reconstruction with motion estimation. This paper describes an alternative formulation for dense scene flow estimation that provides reliable results using only two cameras by fusing stereo and optical flow estimation into a single coherent framework. Internally, the proposed algorithm generates probability distributions for optical flow and disparity. Taking into account the uncertainty in the intermediate stages allows for more reliable estimation of the 3D scene flow than previous methods allow. To handle the aperture problems inherent in the estimation of optical flow and disparity, a multi-scale method along with a novel region-based technique is used within a regularized solution. This combined approach both preserves discontinuities and prevents over-regularization – two problems commonly associated with the basic multi-scale approaches. Experiments with synthetic and real test data demonstrate the strength of the proposed approach.

Neural Pattern Recognition With Multi-Scale Pyramidal Coding and Selective Attention

A new neural network architecture for spatial patttern recognition using multi-scale pyramida1 coding is here described. The network has an ARTMAP structure with a new class of ART-module, called Hybrid ART-module, as its front-end processor. Hybrid ART-module, which has processing modules corresponding to each scale channel of multi-scale pyramid, employs channels of finer scales only if it is necesssary to discriminate a pattern from others. This process is effected by serial match tracking. Also the parallel match tracking is used to select the spatial location having most salient feature and limit its attention to that part.